{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "Loaded 42,075 records\n"
     ],
     "output_type": "stream"
    },
    {
     "name": "stderr",
     "text": [
      "/Users/mkennedy/github/talk-python/courses/pycharm/pycharm-demos/.env/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3051: DtypeWarning: Columns (70,71,72,73,74,76,79) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "file = \"./data/vehicles.csv\"\n",
    "auto = pd.read_csv(file)\n",
    "print(f\"Loaded {len(auto):,} records\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1008x720 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzkAAAJgCAYAAACp5K2SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3X3YXGV9L/rvDUFiCSIggvJikKqABEJNsFSg4Ato6fZlo1bsrlDw5agItXVXtKen1n2q2F2xxdq6baXAKb6BonRDK6KgwpaXAMEIAUSMCCJGVJAiCsl9/liT+CR5YjKTmTzz3M/nc11zZWbNzG9+a82zJvNd91prSq01AAAArdhiqhsAAAAYJiEHAABoipADAAA0RcgBAACaIuQAAABNEXIAAICmCDkAAEBThBwAAKApQg4AANAUIQcAAGjKrKluIEme8IQn1Llz5051GwAAwJi67rrrflhr3WljHjsWIWfu3LlZtGjRVLcBAACMqVLKdzb2sXZXAwAAmiLkAAAATRFyAACApozFMTkAADBTPfLII7nrrrvy8MMPT3UrY2H27NnZbbfdstVWWw1cQ8gBAIApdNddd2XbbbfN3LlzU0qZ6namVK019913X+66667sueeeA9exuxoAAEyhhx9+ODvuuOOMDzhJUkrJjjvuuMmjWkIOAABMMQHnl4axLIQcAABgDWeddVZOOumkqW5jYEIOAADQFCEHAAAatGzZsuy99945/vjj8/SnPz2///u/n0svvTTPec5z8rSnPS3XXHNNrrnmmhx88ME58MAD81u/9Vu59dZb16lz0UUX5eCDD84Pf/jDLF++PMccc0wWLlyYhQsX5sorr5yCOdswZ1cDAIBG3X777TnvvPNy5plnZuHChfnYxz6WK664IhdeeGHe85735JxzzslXv/rVzJo1K5deemne+c535tOf/vTq519wwQU5/fTTc/HFF2f77bfPq1/96rz1rW/NIYcckjvvvDNHHXVUli5dOoVzODkhBwAAGrXnnntm3rx5SZJnPvOZed7znpdSSubNm5dly5bl/vvvz3HHHZdvfvObKaXkkUceWf3cL33pS1m0aFEuueSSPO5xj0uSXHrppbn55ptXP+aBBx7Igw8+mDlz5mzeGdsAIQcAABq19dZbr76+xRZbrL69xRZb5NFHH82f//mf54gjjsgFF1yQZcuW5fDDD1/9+L322it33HFHbrvttixYsCBJsnLlylx11VWZPXv2Zp2PfjkmBwAAZqj7778/u+66a5LujGoTPeUpT8mnP/3pvOY1r8lNN92UJDnyyCPzwQ9+cPVjFi9evNl67YeQAwAAM9Sf/umf5h3veEcOPPDAPProo+vcv/fee+fcc8/NK17xinzrW9/KGWeckUWLFmX//ffPvvvumw9/+MNT0PWGlVrrVPeQBQsW1EWLFk11GwAAsNktXbo0++yzz1S3MVYmWyallOtqrQs25vlGcgAAgKYIOQAAQFOEHAAAoClCDgAA0BQhBwAAaIqQAwAANEXIAQCAGW7LLbfM/PnzV1+WLVs2cK3jjz8+559//vCaG8CsKX11AABgDXNPvWio9ZaddvQGH/PYxz42ixcv7rv2o48+mlmzxi9SjF9HPWu/uRvz5gAAAMPx8MMP541vfGMWLVqUWbNm5fTTT88RRxyRs846K5/5zGfy4IMPZsWKFbn88svzlre8JV/4whey++675zGPecxUtz6+IQcAANg8fvazn2X+/PlJkj333DMXXHBBPvShD6WUkiVLluSWW27JkUcemdtuuy1Jcv311+frX/96dthhh3zmM5/Jrbfemptvvjn33ntv9t1335xwwglTOTtCDgAAzHST7a52xRVX5C1veUuSZO+9985TnvKU1SHnBS94QXbYYYckyVe+8pUce+yx2XLLLfPkJz85z33uczdv85Nw4gEAAKAv22yzzVS38CsJOQAAwDoOPfTQnHvuuUmS2267LXfeeWee8YxnrPO4ww47LJ/85CezYsWK3HPPPbnssss2d6vrsLsaAACwjje96U154xvfmHnz5mXWrFk566yzsvXWW6/zuJe97GX50pe+lH333Td77LFHDj744Cnodk2l1jrVPWTBggV10aJFa0xzdjUAAGaCpUuXZp999pnqNsbKZMuklHJdrXXBxjzf7moAAEBThBwAAKApQg4AANAUIQcAAGiKkAMAADRFyAEAAJoi5AAAwAw3Z86cqW5hqPwYKAAAjJN3bTfkevcPt940YCQHAABYx/Lly3PMMcdk4cKFWbhwYa688sokySmnnJJ3v/vdSZLPf/7zOeyww7Jy5cocf/zxOf/881c/f9Xo0D333JPDDjss8+fPz3777ZevfvWrI+/dSA4AALCOU045JW9961tzyCGH5M4778xRRx2VpUuX5r3vfW8WLlyYQw89NCeffHIuvvjibLHF+sdOPvaxj+Woo47Kn/3Zn2XFihV56KGHRt67kAMAAKzj0ksvzc0337z69gMPPJAHH3wwc+bMyT/90z/lsMMOywc+8IHstddev7LOwoULc8IJJ+SRRx7JS1/60syfP3/UrdtdDQAAWNfKlStz1VVXZfHixVm8eHHuvvvu1bugLVmyJDvuuGO+973vrX78rFmzsnLlytXP/cUvfpEkOeyww/KVr3wlu+66a44//vicc845I+9dyAEAANZx5JFH5oMf/ODq24sXL06SfOc738n73//+3HDDDfn3f//3XH311UmSuXPn5rrrrkuSXHjhhXnkkUdWP37nnXfO6173urz2ta/N9ddfP/LehRwAAJjhHnrooey2226rL6effnrOOOOMLFq0KPvvv3/23XfffPjDH06tNSeeeGL+5m/+Jk9+8pPz0Y9+NK997Wvz8MMP53Wve12+/OUv54ADDsjXvva1bLPNNkmSyy+/PAcccEAOPPDAfPKTn8wpp5wy8vkptdaRv8iGLFiwoC5atGiNaXNPvWiN28tOO3pztgQAAJvF0qVLs88++0x1G2NlsmVSSrmu1rpgY55vJAcAAGiKkAMAADRFyAEAAJoi5AAAAE0RcgAAgKYIOQAAQFNmTXUDAADA1LjvvvvyvOc9L0ny/e9/P1tuuWV22mmnJMk111yTxzzmMZtU/4477sg111yTV73qVZvcaz+EHAAAGCPzzp431HpLjluy3vt23HHHLF68OEnyrne9K3PmzMnb3va2ob32HXfckU984hObPeTYXQ0AAFjHX//1X2e//fbLfvvtlw9+8INJkne+8535+7//+9WPefvb354PfehDWblyZf74j/84++23X+bNm5fzzz8/SXLqqafmsssuy/z583PGGWfkW9/6Vg499NAceOCBedaznpWrr756JL0byQEAANZw9dVX59xzz821116bRx99NAcddFAOP/zwnHDCCTn22GNz0kknZcWKFTnvvPNy3XXX5bzzzsvSpUtz4403Zvny5Vm4cGEOO+ywnHbaafn7v//7fPazn02SPPTQQ/nCF76Q2bNn55Zbbslxxx03kqAj5AAAAGu44oorcswxx+Sxj31skuSlL31pvvrVr+ZNb3pTtt122yxZsiTf+c53ctBBB2X77bfPFVdckWOPPTZbbrlldtlllxxyyCFZtGjROsf0/PznP89JJ52UG2+8MbNmzcq3vvWtkfQv5AAAABvtxBNPzFlnnZVly5blDW94Q1/Pff/735/dd989//qv/5pHHnkkc+bMGUmPjskBAADWcOihh+aCCy7Iz372szz44IP53Oc+l0MPPTRJcswxx+Tf/u3fsnjx4jz/+c9f/fhPfOITWblyZe69995ceeWVWbBgQbbddtv89Kc/XV33/vvvz5Oe9KSUUnL22Wen1jqS/o3kAAAAazjooINy7LHHZuHChUmSN77xjZk3rzvr2+zZs3PYYYdll112yRZbdGMmL3/5y3PVVVdl//33Tyklp59+ep74xCfm8Y9/fFasWJEDDjggJ554Yk466aS8/OUvz5lnnpmjjz46W2+99Uj6L6NKT/1YsGBBXbRo0RrT5p560Rq3l5129OZsCQAANoulS5dmn332meo2NtrKlSszf/78fPazn81Tn/rUkbzGZMuklHJdrXXBxjzf7moAAMBGWbJkSfbaa6+88IUvHFnAGQa7qwEAABtl3rx5+fa3vz3VbWyQkRwAAKApQg4AAEyxcThOflwMY1kIOQAAMIVmz56d++67T9BJF3Duu+++zJ49e5PqOCYHAACm0G677Za77rory5cvn+pWxsLs2bOz2267bVINIQcAAKbQVlttlT333HOq22iK3dUAAICmCDkAAEBThBwAAKApQg4AANAUIQcAAGiKkAMAADRFyAEAAJoi5AAAAE0RcgAAgKYIOQAAQFOEHAAAoClCDgAA0BQhBwAAaIqQAwAANEXIAQAAmiLkAAAATRFyAACApgg5AABAU4QcAACgKUIOAADQFCEHAABoipADAAA0RcgBAACaIuQAAABNEXIAAICmCDkAAEBThBwAAKApQg4AANAUIQcAAGiKkAMAADRFyAEAAJqywZBTStm9lHJZKeXmUspNpZRTetN3KKV8oZTyzd6/2/eml1LKGaWU20spXy+l/MaoZwIAAGCVjRnJeTTJn9Ra903ym0neXErZN8mpSb5Ya31aki/2bifJi5I8rXd5fZJ/HHrXAAAA67HBkFNrvafWen3v+k+TLE2ya5KXJDm797Czk7y0d/0lSc6pnauSPL6U8qShdw4AADCJvo7JKaXMTXJgkquT7Fxrvad31/eT7Ny7vmuS70542l29aQAAACO30SGnlDInyaeT/FGt9YGJ99Vaa5LazwuXUl5fSllUSlm0fPnyfp4KAACwXhsVckopW6ULOOfWWj/Tm3zvqt3Qev/+oDf97iS7T3j6br1pa6i1fqTWuqDWumCnnXYatH8AAIA1bMzZ1UqSjyZZWms9fcJdFyY5rnf9uCSfmzD9Nb2zrP1mkvsn7NYGAAAwUrM24jHPSfIHSZaUUhb3pr0zyWlJPlVKOTHJd5K8snffxUl+J8ntSR5K8odD7RgAAOBX2GDIqbVekaSs5+7nTfL4muTNm9gXAADAQPo6uxoAAMC4E3IAAICmCDkAAEBThBwAAKApQg4AANAUIQcAAGiKkAMAADRFyAEAAJoi5AAAAE0RcgAAgKYIOQAAQFOEHAAAoClCDgAA0BQhBwAAaIqQAwAANEXIAQAAmiLkAAAATRFyAACApgg5AABAU4QcAACgKUIOAADQFCEHAABoipADAAA0RcgBAACaIuQAAABNmTXVDWxOc0+9aPX1ZacdPYWdAAAAo2IkBwAAaIqQAwAANEXIAQAAmiLkAAAATRFyAACApgg5AABAU4QcAACgKUIOAADQFCEHAABoipADAAA0RcgBAACaIuQAAABNEXIAAICmCDkAAEBThBwAAKApQg4AANAUIQcAAGiKkAMAADRFyAEAAJoi5AAAAE0RcgAAgKYIOQAAQFOEHAAAoClCDgAA0BQhBwAAaIqQAwAANEXIAQAAmiLkAAAATRFyAACApgg5AABAU4QcAACgKUIOAADQFCEHAABoipADAAA0RcgBAACaIuQAAABNEXIAAICmCDkAAEBThBwAAKApQg4AANAUIQcAAGiKkAMAADRFyAEAAJoi5AAAAE0RcgAAgKYIOQAAQFOEHAAAoClCDgAA0BQhBwAAaIqQAwAANEXIAQAAmiLkAAAATRFyAACApgg5AABAU4QcAACgKUIOAADQFCEHAABoipADAAA0RcgBAACaIuQAAABNEXIAAICmCDkAAEBThBwAAKApQg4AANAUIQcAAGiKkAMAADRFyAEAAJoi5AAAAE0RcgAAgKYIOQAAQFOEHAAAoClCDgAA0BQhBwAAaIqQAwAANGWDIaeUcmYp5QellG9MmPauUsrdpZTFvcvvTLjvHaWU20spt5ZSjhpV4wAAAJPZmJGcs5K8cJLpH6i1zu9dLk6SUsq+SV6V5Jm95/xDKWXLYTULAACwIRsMObXWryT50UbWe0mST9Raf15r/XaS25MctAn9AQAA9GVTjsk5qZTy9d7ubNv3pu2a5LsTHnNXbxoAAMBmMWjI+cckeyWZn+SeJO/vt0Ap5fWllEWllEXLly8fsA0AAIA1DRRyaq331lpX1FpXJvmn/HKXtLuT7D7hobv1pk1W4yO11gW11gU77bTTIG0AAACsY6CQU0p50oSbL0uy6sxrFyZ5VSll61LKnkmeluSaTWsRAABg483a0ANKKR9PcniSJ5RS7kryF0kOL6XMT1KTLEvyhiSptd5USvlUkpuTPJrkzbXWFaNpHQAAYF0bDDm11mMnmfzRX/H4v0ryV5vSFAAAwKA25exqAAAAY0fIAQAAmiLkAAAATRFyAACApgg5AABAU4QcAACgKUIOAADQFCEHAABoipADAAA0RcgBAACaIuQAAABNEXIAAICmCDkAAEBThBwAAKApQg4AANAUIQcAAGiKkAMAADRFyAEAAJoi5AAAAE0RcgAAgKYIOQAAQFOEHAAAoClCDgAA0BQhBwAAaIqQAwAANEXIAQAAmiLkAAAATRFyAACApgg5AABAU4QcAACgKUIOAADQFCEHAABoipADAAA0RcgBAACaIuQAAABNEXIAAICmCDkAAEBThBwAAKApQg4AANAUIQcAAGiKkAMAADRFyAEAAJoi5AAAAE0RcgAAgKYIOQAAQFOEHAAAoClCDgAA0BQhBwAAaIqQAwAANEXIAQAAmiLkAAAATRFyAACApgg5AABAU4QcAACgKUIOAADQFCEHAABoipADAAA0RcgBAACaIuQAAABNEXIAAICmCDkAAEBTZk11A9PZ3FMvWuP2stOOnqJOAACAVYzkAAAATRFyAACApgg5AABAU4QcAACgKUIOAADQFCEHAABoipADAAA0RcgBAACaIuQAAABNEXIAAICmzJrqBljT3FMvWn192WlHT2EnAAAwPRnJAQAAmiLkAAAATRFyAACApjgmh7HgWCQAAIbFSA4AANAUIQcAAGiKkAMAADRFyAEAAJoi5AAAAE0RcgAAgKY4hfQM4PTMAADjZeL3s8R3tGEzkgMAADRFyAEAAJoi5AAAAE0RcgAAgKYIOQAAQFOEHAAAoClOIU3fnPIQAIBxZiQHAABoipADAAA0RcgBAACaIuQAAABNEXIAAICmCDkAAEBThBwAAKApQg4AANAUIQcAAGiKkAMAADRl1oYeUEo5M8nvJvlBrXW/3rQdknwyydwky5K8stb641JKSfJ3SX4nyUNJjq+1Xj+a1mH95p560Rq3l5129BR1AgDA5rYxIzlnJXnhWtNOTfLFWuvTknyxdztJXpTkab3L65P843DaBAAA2DgbDDm11q8k+dFak1+S5Oze9bOTvHTC9HNq56okjy+lPGlYzQIAAGzIoMfk7Fxrvad3/ftJdu5d3zXJdyc87q7eNAAAgM1ik088UGutSWq/zyulvL6UsqiUsmj58uWb2gYAAECSwUPOvat2Q+v9+4Pe9LuT7D7hcbv1pq2j1vqRWuuCWuuCnXbaacA2AAAA1rTBs6utx4VJjktyWu/fz02YflIp5RNJnp3k/gm7tQEAwLQ08cytzto6/jbmFNIfT3J4kieUUu5K8hfpws2nSiknJvlOklf2Hn5xutNH357uFNJ/OIKeAQAA1muDIafWeux67nreJI+tSd68qU0BAAAMapNPPAAAADBOhBwAAKApg554AAAAGCNOjvBLRnIAAICmGMmBjWTrCADA9GAkBwAAaIqRHJgiE0eGEqNDAADDYiQHAABoipEcAJI47gyAdhjJAQAAmiLkAAAATRFyAACApgg5AABAU4QcAACgKUIOAADQFKeQBmDacJprADaGkRwAAKApQg4AANAUIQcAAGiKkAMAADRFyAEAAJri7GoAjMTEM6ElzoYGbD4+fzCSAwAANMVIDrBetoQBsDb/NzAdGMkBAACaIuQAAABNEXIAAICmCDkAAEBTnHgAGjLxYNBxPRB0OvQIAExvRnIAAICmCDkAAEBThBwAAKApjskBpj3H+QCsn89IZiIjOQAAQFOEHAAAoClCDgAA0BQhBwAAaIoTDwAwY008IDtxUDZAK4zkAAAATTGSAzANGYGAqWc9hPFlJAcAAGiKkRyAzcCP8QHA5mMkBwAAaIqRHACAMWHUF4bDSA4AANCUaTmSM+/seWvcXnLckinqBACATWUEi2EzkgMAADRFyAEAAJoyLXdXAxglP/AHANObkRwAAKApQg4AANAUIQcAAGiKkAMAADRFyAEAAJoi5AAAAE0RcgAAgKYIOQAAQFOEHAAAoClCDgAA0JRZU90AU2fe2fPWuL3kuCVT1AkAAAyPkRwAAKApQg4AANAUIQcAAGiKY3IAAIB1zD31ojVuLzvt6CnqpH9GcgAAgKYYyQGAIZq45XM6bfUEaImRHAAAoClCDgAA0BQhBwAAaIqQAwAANMWJBxiqeWfPW319yXFLprATAABmKiM5AABAU4QcAACgKUIOAADQFMfkTCOOdwFgGCb+YGniR0uB9hjJAQAAmmIkBwDG3MSRF6MuwCBm2h5BRnIAAICmGMkBBjJxi1AyM7YKAQDTg5EcAACgKUZyembafooAANAqIzkAAEBThBwAAKApdlcDAIAx4uQ+m85IDgAA0BQjOTCGbMEBmN5G8Tk+7JMk+b9meGbiCazG/e/HSA4AANAUIzmMvemw5WombsGBcWM9hKlnPWRcGMkBAACaYiRnRMZ9P0UAmK6MyAMbYiQHAABoipEcAJiEEfn2zD31otXXl5129BR2AoyakRwAAKApQg4AANAUu6sBY2M6HPg73XpMxrdPABgVIzkAAEBTjOQAzTKiAZuPg/qBcWIkBwAAaIqRHAA2aDocizQdWI7QHyPyDMpIDgAA0BQhBwAAaMom7a5WSlmW5KdJViR5tNa6oJSyQ5JPJpmbZFmSV9Zaf7xpbQIAAGycYRyTc0St9YcTbp+a5Iu11tNKKaf2br99CK8DQCPsZw/AKI1id7WXJDm7d/3sJC8dwWsAAABMalNDTk1ySSnlulLK63vTdq613tO7/v0kO2/iawAAAGy0Td1d7ZBa692llCcm+UIp5ZaJd9ZaaymlTvbEXih6fZLssccem9gGwPTltMKME3+PtM7f+MywSSM5tda7e//+IMkFSQ5Kcm8p5UlJ0vv3B+t57kdqrQtqrQt22mmnTWkDAABgtYFHckop2yTZotb60971I5O8O8mFSY5Lclrv388No1Fg09hyBUyVQT5/5p560Rq3l5129FB7Atq2Kbur7ZzkglLKqjofq7X+Rynl2iSfKqWcmOQ7SV656W0CAABsnIFDTq31jiQHTDL9viTP25SmAAAABjWKU0gDAABMmWH8GCgAMAX8qCrA5IzkAAAATRFyAACApgg5AABAU4QcAACgKUIOAADQFCEHAABoipADAAA0RcgBAACaIuQAAABNEXIAAICmCDkAAEBThBwAAKApQg4AANAUIQcAAGiKkAMAADRFyAEAAJoi5AAAAE2ZNdUNAAAAQ/au7da8veceU9PHFDGSAwAANEXIAQAAmiLkAAAATXFMDgAAsGETj/MZ82N8jOQAAABNMZIDAABTbRqNkkwHRnIAAICmCDkAAEBT7K4GAEDb7Ao24xjJAQAAmiLkAAAATRFyAACApgg5AABAU4QcAACgKc6uBgDA+JgOZ0Kb2GMyvn3OYEZyAACApgg5AABAU4QcAACgKUIOAADQFCEHAABoipADAAA0RcgBAACaIuQAAABNEXIAAICmCDkAAEBThBwAAKApQg4AANAUIQcAAGiKkAMAADRl1lQ3AADANPau7X55fc89pq4PmMBIDgAA0BQhBwAAaIrd1QAAhsFuWzA2jOQAAABNMZIDAMw8E0ddkpkz8mK0iRnCSA4AANAUIzkAwHCNYpTECATQByM5AABAU4QcAACgKUIOAADQFMfkAMBM53iX8TRTzwAHQ2AkBwAAaIqQAwAANEXIAQAAmiLkAAAATXHiAQCYTpwkAGCDjOQAAABNEXIAAICmCDkAAEBThBwAAKApQg4AANAUIQcAAGiKkAMAADRFyAEAAJoi5AAAAE0RcgAAgKbMmuoGAGBsvGu7X17fc4/h1htWTQA2yEgOAADQFCEHAABoipADAAA0xTE5AExPjncBYD2M5AAAAE0xkgPA5jHsM5cBwHoYyQEAAJoi5AAAAE2xuxrA5jaKA+btCgYAqxnJAQAAmmIkB4B1OT0zANOYkRwAAKApRnJgXMzEYyqmy7EpM/G9AYBpzEgOAADQFCM5MIjpcLzCdBklAQAYMiM5AABAU4QcAACgKUIOAADQFCEHAABoipADAAA0RcgBAACa4hTSjB+nPgYAYBMYyQEAAJoyspBTSnlhKeXWUsrtpZRTR/U6AAAAE40k5JRStkzyoSQvSrJvkmNLKfuO4rUAAAAmGtVIzkFJbq+13lFr/UWSTyR5yYheCwAAYLVRhZxdk3x3wu27etMAAABGqtRah1+0lJcneWGt9bW923+Q5Nm11pMmPOb1SV7fu/mMJLduROknJPnhkNudDjX1OJ71pktNPY5nvVHUnA49jqKmHsez3ihqToceR1FTj+NZbxQ1p0OPo6i5sfWeUmvdaWMKjuoU0ncn2X3C7d1601artX4kyUf6KVpKWVRrXbDp7U2vmnocz3rTpaYex7PeKGpOhx5HUVOP41lvFDWnQ4+jqKnH8aw3iprTocdR1BxFj6PaXe3aJE8rpexZSnlMklcluXBErwUAALDaSEZyaq2PllJOSvL5JFsmObPWetMoXgsAAGCiUe2ullrrxUkuHnLZvnZva6imHsez3nSpqcfxrDeKmtOhx1HU1ON41htFzenQ4yhq6nE8642i5nTocRQ1h97jSE48AAAAMFVGdUwOAADAlBByAACApgg5AABAU0Z24gEARquUsl2SFybZtTfp7iSfr7X+ZOq6Gr1Syi5JUmv9fillpySHJrl1WGfxLKW8p9b6zmHUmmlKKYclubfWemsp5TlJDk6ytNZ60RS3BswwTjwAMA2VUl6T5C+SXJKLcQg8AAAWdElEQVRf/tjybklekOQva63nDPn1XlBr/cIAz3tckp1qrd9aa/r+tdavD1DvDUlOTVKSvC/J8Um+keSQJH9da/1on/XOWHtSkj9Ick6S1FpP7rfHSV5jzyQHJrm51nrLgDX2SPKDWuvDpZSSbr5/I8nNSf6p1vpon/VenOSSWuvDg/Sznpp/m+SgdBtQP5/keUn+PclvJ7mh1vrfB6g5J12Q3z3JiiS39fpeuQl97p3kJVlz48CFtdalg9Zcz+v8Ya31XwZ87t7p+ru61vrghOkvrLX+xwD1DkpSa63XllL2TbdMb+mdCXcoSinn1FpfM8R6h6T7e/pGrfWSAZ7/7HQB+4FSymPTfW6sWmfeU2u9f4CaJye5oNb63X6fu556q35L8nu11ktLKa9O8ltJlib5SK31kQHrPjXJf82a683Haq0PDFhvs6wzvdcaeL1Zp9Z0CznDXIk2dQXq1RjqSjTsFahXc+gr0bBXoF7No5K8NGuuRJ8b5AN9I17r/6m1vnuA5x2V7ovkF2utyyZMP6HWeuYA9UqSVySpSc5P8tx0HyS3JPnwpvxHPuE1vlRrfe4mPP8JtdYfTrj939Jbb9J9uerrQ6SU8rIkX661/qi3Ff796X0BTPIntda7Bujx9CSfrrVe2e9z11NvhyQnJfleko8meWd6W6TTrdc/HrDuEUmOyZrrzT/XWm8foNatSZ699qhNKWX7dF+Mnj5Ij7/i9e6ste7R53NemeRvk/wgyVZJjq+1Xtu77/pa628M0MeSJM9O8tgk30ny670Rne2TXFZrnd9nve8m+XK6sFh6k/8myduSpNZ69gA9frbW+tLe9ZekWwaXp/vcfW+t9awBan4jyUG11odKKe9LsleSz6b7zEit9YQ+6/0syX+mCyEfTzcCuKLfvtaqeVOS/dK9N3cn2bXX71bpQs5+fdZ7Zbr34etJjkjyf9LtZj8vye/XWpcM0OPbkxyb5BNJVn3W7Jbu/8hP1FpP67fmr3itvteZ3vNOTvLmdJ8385OcUmv9XO++vtebUspfJHlRuvD5hXTrz2XpNoh8vtb6VwP0uPYPvJd079GXkqTW+uIBal5Taz2od/116ZbBBUmOTPJv/b43vb/HA3q/3fiRJA+l+3/2eb3p/3WAHu9Pt958K916c16tdXm/dSbUOzfd+/JrSX6SZE6Sz/R6LLXW4waoeXKS303ylSS/k+SGXu2XJXlTrfXyPutttnWm93oDrTeT1hrnkDPslWjYK1CvzlBXomGvQL2aQ12Jhr0C9Wr+bZKnp9t6OnElek2Sb9ZaT+m35gZeb5AvbO9Jt7X4+iT/Jcnf1lo/2Ltv0C9s/5DkiUkek+SBJFsnuTDJ0el2+ehrvkspa28ZL+mW661JUmvdf4AeV89bKeX/Trdr0MfS/Q3cVWt9a5/1bq617tu7/skkVyU5L8nz031xecEAPS5P94V3pySfTPLxWusN/daZUO/iJEuSPC7JPr3rn0r3peCAWutLBqj53iS7JPliujD/7XQh503pgtN5fda7LcnCtTek9HZhW1RrfdoAPa79mbv6riTPrbVu02e9xUleVGu9p7cl+Zwk76i1XlBKuaHWeuAAPU78e7yx1nrAhPv6rllK2TbJ/0i3Hr6t1vq9Usodtdan9tvbZH2UUv5Pur/rb5dSnpBuA8kBv7rCpDUnrjfXpXvvV/Zu39hvzVLKDekC0svTfVnZL93/hx+vtX653/56Nb9Ra92vlDI7yT1Jnlxr/VkpZcskS1b130e9ryf5zV5QekKSc2utR5VS9k+3Eei3BujxtiTPXHvjXm9j4E39rjeTfOauvivJ02utWw/Q45IkB9daHyylzE33neL/q7X+3YB/40vShaWtk3w/yW4TNsxePej/C+k2TP1zuo10Jd13llclySB/Q2utN9cm+Z1a6/JSyjZJrqq1zuuz3tJa6z6r+p34f3QpZXG/G0RW9ZjkWen+v/q9JC9Ocl26ef9MrfWnfdb7eq11/1LKrHQbBp5ca13R2wB644DvzZIk83t1fi3JxbXWw0s3Gvy5Af5+hrrO9J479PVmUrXWsb2k+zL5r0kOTzfcfXi6D87fTvLbA9S7YcL1a9PtQpEk26T7AB6kx6UT+13rvsWD9JhuS9WR6bYgL0/yH0mOS7LtgD1+vffvrCT3Jtmyd7usuq/Peksm1Pi1JJf3ru8xcRn3WfO29Uwv6ULOIDUfWM/lp0keHXC+Z/WuPz7dj91+YO2/rX5r9v7dKsl9SR4z4b0a5L25sLfO7J3kKUnmJvlu7/pTBuxx4npzfZJtJvTc93qT7tiJVdevW+u+vteZiT2mC3R/nuSmdKNhf5HuA7Pfeosn/P3dPaQel0y4PivJlb3r26cbTe633nHpNob8Y7qRpncm+XBv2vED9vjjdAH7t9e6HJ4udA88z73bT0r3heDkrPV52UfN65Js1bu+24Tps9N9Kei7Zu/5z0q3dfttSZYNWqdX6/oJ16+Z7G91gJqfTxc0k+TTq9bnJDsOMt9rL/90AfzkJF9L8t0Be3xfkq+m+//1fyb5tyR/lm6U7MOD/P3klxtjH7vWZ1Hf60zvebdM9lnY+4y8dYB696YLEE9Z6zI33d4Tg/R401q356T7HnD6IJ8/ay23G9a6b9DPsy2SvDXdyND83rQ7Bqk1oeaNvc/DHdNtqJl0Hvqod16SP+xd/5ckC3rXn57k2gF7XHu92Spd0Pl4kuUD1PtGuo2c26f7brJDb/rsTPh+2WfNJUm27l3ffuKyHGS9GfY603vu0NebyS7jfuKBBUlOSfch+d9rrYtLKT+rA25lSrJFb5eGLdJ9cC5Pklrrf5ZS+tqfeYJvTNh/8MZSyoJa66JSytOTDLIvZa3d1rlLklzSG+Z/Ubqhwr9Jt6W6X1v0Evc26ULJdkl+lG6rzlYD1Eu6L2krejXm9Bq/s9fvIB4upSysvV1ZJliYZNB9xn+SbmvnvWvf0dtFpV+zam+/91rrT0op/yXJR0op56X7kBrEqnqPlFKurbX+onf70VJK37uq1Vpf3Nsd7CNJ/qbWemEp5ZFa63cG7C9JHltKOTDderNlrfU/J/Q8yO4tl5dS3p3kvb3rL6vdlv0jkvS9j3RP7fV0W7qt8v+jt7X32HRh9Nf7rLfqs2LbJHNKKXNrrctKKTtm8Pd6ZSllh1rrj5I8OcmWvZ5/3Ntq15da69m9kZej8stdPC9PN1Iy0O506UbVHprsM7Z0u8f166ellL1q73ic2o3oHJ5uN6tnDtjjy1ZdqWvu2rhjkj8ZsGZqrdeVUp6bbmTtikHr9BxQSnkgXUjeupTypN68Pya9930Ar01yTinlXenWk8W9kbLHJ/njAeqt8TdXa/1+kjOSnFFKecogDdZa315KObi7Wq8qpeyV7v3653SjEf26OMl/lFK+ku4YkvOS1buT9r3O9PxRki+WUr6ZbgNQ0m2g+/V0u6j2638nmVNrXbz2HaWUywfs8d5SyvxVNWs3ovO7Sc5Mt6tev35RSvm1WutD6cL8qv62SzLQLtG97ykf6P3/94FSyr3Z9JNZbZduI0ZJUiesN3My2Pv92iR/19sD4YdJvtb7v/+7vfsGsfZ680i6jYsX9kZN+vXRdCFiy3Tfdc8rpdyR5DfT7R42iH9Ocm0p5ep0e168L0lKt3v4jwaoN+x1JhnNerOOsd5dbZVSym5JPpAu+b24DrivXillWboVuqT7UvScCSvQFXWwocvtkvxduj+kH6Y7HmfVSnRyrfXGPuutdyh6wodUvz2+Nclb0q1E7093zMeqlej8Wutf9lnvlCQnJlm9AtVa/6W3An261nrYAD3+Rrot0tvml7ur7Z7uP/M311qvG6Dm/5vuwLhrJrnvfbXWt/dZ738n+Z9rfwHsvc47a619n5K9lPLvSV5RJxxY2pu+S6/3g/qt2Xv+Num+7O+V5Fm11t0GqdOrddlak17dW292TLc/94I+622V7sN81TEEu6XbRfPfkpxaa71zgB4H2vXpV9Q7Nt1xFEn3pfeN6T4z9k13UP9HBqj5e0n+Ot0uas9I8sZa60W99ebvaq2vHrDXnTPhOLbJQv1UKaUckC40fXOt6VsleWWt9dxNqD3U+d4cy7GU8vgk+9Rav7YJNfZJtyV6VrrPymvrAMfulVIOrwPsWryRtYe2LEspv5Nuvbux9k58UUrZIt1o3s8HrLlFuuMKJx7/eW3dxGOShqX3nefRXvBc+77n1D6PPSylbD3ZsirdLoBPqgMc2zRJraPTfaca+lkJe+Fh51rrtwd8/uOS7JneOrOJf49P721MG5pSypOTpHa7yj4+3a5wd0723aWPms9Mt6v1N+qAJztZq95YrzPrMy1CziqjWok2dQXq1RjKSjSKFahXd6gr0bBXoAl1d8ma/zmu8yE/VUq3/3JqrT+b5L5da613r/usgV9rm3S7hf1gE+sckG7f7g8Pp7M1am+Zbki87+A9ocZ26UbI7tvEXuasHRQ3VW/+Sm9UbVa6ofW7a633bELNHZI8NcntdRNP81xKmZ9u97Tt0n3ZLekC40/SHRt3/SbUHtsAsdZ8Tzyr3E/SBce+jsXaQL2xWo6jqLkZ35uBl+XmCvLD/hwZ0efSjOtxFDX1ODOMfcgp3YFSD/R2D5qbbhe2W2qt3xiHenocXo+9ugsy4cxTQ9oCMdSaetTjONQs3a5Kb6i1Xr3W9N9M8r/qYAe3H5huRHUoX1KHHUh6NYc631OwHAed76EGiFEEkhG8NyMLoOt5vaGd1WkU9UZRczr0OIqaeuzrOfun2wV+13RnY3x77e0SXSac0Guqa05mrI/JKaWcmuQNSX5eSll1Ss8rk/xlKeWjtdbTp7KeHofa42+n25XuJ+n2Gb4yyfallEeS/EEd4JTaw66pRz2OU4/pRvquXnti7Y6F6OssaBP8S9b/JfVfkvT7hf+sX1HvrAHqJcOf7829HM/KYPN91q+oOez3ZpB6yfCX5dB7LKWs7/ilkt7xpVNZbxQ1p0OPo6ipx6HV/Ick70p3zOZrk1xRSnlx7Y61HPQ47FHUXMdYj+SU7vTMC9IdLL8syVPrL08neHXt/5z7Q62nx6H2eEOSI3t19kxyeq31ZaWUF6Q76cSRU11Tj3ocsx7PSHfM1Tn55cGgu6c77fq3a619HxBaSvlmXc/pQEspt9da+zqBw7Dr9Z431PmeDstxFDWnyXszih4fTnfmt8lONvTWWuvjp7LeTO1xFDX1OLQe1z5V/xHpRmH+IMk/1MF+PmPoNScz1iM5SVbU7hz7v0jys3Sn2F11NrRxqKfH4fW4Zf3l7wHdme5Ugqm1fqF0v6EzDjX1qMex6bHWenIp5UVZ91eoP1QH/xXzfy+lXJTJv6QO8qO8w6439PmeJstxFDXH/r0ZRY/pToP/2TrJyWxKKYOccWvY9UZRczr0OIqaehxSzVLKdrX3m2y11stKKcekO5X9DgP2OJKa67xGHe+RnLPSna51m3Q/svloug+256b7zZhXTmU9PQ61xzPTnb3qS+nOOX93rfWPS3dSiOtrrXtPdU096nGcehyV9XxJvXDQL/zDrjddjGK+Z+J7M4J5fkaSH9VJfmS7lLJz7fOkBsOuN1N7HEVNPQ6tx1en+w2kq9aavkeSP6+1vm6AHodec9LXGfOQMyvJK9J9MTg/ybPT/e7Fnem2DP3nVNbT41B73CrJ69I7VWiSM2v3a72PTfLEOsDvvAy7ph71OGY9bpfkHem+AO6cbn38QZLPJTmtbuLZ28bVsOd7pi7HUbAsgbFSh/Sroi4uLi4um++S5PNJ3p5klwnTdklyapJLBqy5XZLTkixN96Nx9/Wun5bk8VNdbxTzPR2W4wx+b0a5HG8Z8nIcSr2Z2uNMnW89Dq/mZJe+f7xwcyqlzCmlvLuUclMp5f5SyvJSylWllOPGoZ4eR9LjN9aqefy41NSjHsepxyRza63vqxN+S6rW+v1a62npHe8zgE8l+XGSI2qtO9Rad0xyRLozwn1qDOolw5/v6bAcR1FzOrw3o1yOh69V88cD1hx2vZna4yhq6nHm9Di5YaWlUVzSDXEfn+68+H+c5M+TPC3J2UneM9X19Di+Pc7U+dbjjOrxkiR/mu6HjFdN2zndlvRLB+zx1kHu21z1RjHf02E5zuD3ZqYuxxnX40ydbz0Or+aktYZVaBSXJDeudfva3r9bpPvhySmtp8fx7XGmzrceZ1SP2yd5X7rh/h+nG/Jf2pu2w4A9TocAMdT5ng7LcQa/NzN1Oc64HmfqfOtxeDUnu4z17mpJ/rOUckiSlFJenO4DM7XWlUkGOVfxsOvpcXx7HEVNPepxbHqs3a9D/0uSk5LsXrsh/31qrW9PMuivRf9ekh2TfLmU8uNSyo+SXJ7ulJ59nzVxBPWGPt/TZDmOoubYvzej6HEENfU4vjX1OHN6nNyw0tIoLkn2T3JNui1CVyR5em/6TklOnup6ehzfHmfqfOtxRvV4cpJbk3w23Y/yvmTCfdcP0mPvuXsneX6SOWtNf+GY1BvqfE+X5TgT35uZuhxnao8zdb71OLya67zGsApt7kuSPxznenqcWTX1OJ71Wu4xyZJV/zkkmZtkUZJTerdvGLCPsQ8Qw57v6bAcZ/B7M1OX44zrcabOtx6Hu3FpndcZVqHNfUly5zjX0+PMqqnH8azXco9Jblrr9px0P8p7epLFA/YxHQLEUOd7OizHGfzezNTlOON6nKnzrcfh1ZzsMitjrJTy9fXdle4ApSmtN4qaehzfmnocTk09Dq3mvaWU+bXWxUlSa32wlPK7Sc5MMm+QHpNsUWt9sFdvWSnl8CTnl1KeksGORRp2vWT48z0dluMoak6H92amLseZ2OMoaupx5vQ4uWGlpVFcktybZH668+tPvMxN8r2prqfH8e1xps63HmdUj7tlwo8urnXfcwbs8UtJ5q81bVaSc5KsmOp6o5jv6bAcZ/B7M1OX44zrcabOtx6HV3PS1xlWoVFcknw0ySHrue9jU11Pj+Pb40ydbz3OnB5Hcck0CBDT4TKK+Z6J781MXY4zsceZOt96HF7NyS6lVxAAAKAJ4/47OQAAAH0RcgAAgKYIOQAAQFOEHACmtVLKllPdAwDjRcgBYLMppby7lPJHE27/VSnllFLKfy+lXFtK+Xop5S8n3P/ZUsp1pZSbSimvnzD9wVLK+0spNyY5eDPPBgBjTsgBYHM6M8lrkqSUskWSVyX5fpKnJTko3W8JPauUcljv8SfUWp+VZEGSk0spO/amb5Pk6lrrAbXWKzbnDAAw/mZNdQMAzBy1+3Xr+0opBybZOckNSRYmObJ3PUnmpAs9X0kXbF7Wm757b/p9SVYk+fTm7B2A6UPIAWBz++ckxyfZJd3IzvOSvLfW+r8mPqiUcniS5yc5uNb6UCnl8iSze3c/XGtdsbkaBmB6sbsaAJvbBUlemG4E5/O9ywmllDlJUkrZtZTyxCTbJflxL+DsneQ3p6phAKYXIzkAbFa11l+UUi5L8pPeaMwlpZR9knytlJIkDyb5b0n+I8n/VUpZmuTWJFdNVc8ATC+l1jrVPQAwg/ROOHB9klfUWr851f0A0B67qwGw2ZRS9k1ye5IvCjgAjIqRHAAAoClGcgAAgKYIOQAAQFOEHAAAoClCDgAA0BQhBwAAaIqQAwAANOX/B9VkVynwjzaDAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(auto\n",
    "    .groupby(['year', 'make'])\n",
    "    .size()\n",
    "    .unstack('make')\n",
    "    .loc[:,['Ford', 'Lexus', 'Toyota']]\n",
    "    .plot(kind='bar', figsize=(14,10))\n",
    ")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}